Automated PVT Characterization and Flow Metering

ABSTRACT

A pressure-volume-temperature (PVT) modeling system includes a sensing device configured to obtain a fluid measurement of a production fluid in a downhole portion of a well system or in a surface portion of the well system, and a processor comprising a PVT model builder, the processor configured to receive the fluid measurement from the sensing device, apply the fluid measurement as an input into the PVT model builder, and generate a PVT model.

BACKGROUND

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the presently describedembodiments. This discussion is believed to be helpful in providing thereader with background information to facilitate a better understandingof the various aspects of the present embodiments. Accordingly, itshould be understood that these statements are to be read in this light,and not as admissions of prior art.

In oil and gas reservoirs, production fluids can have differentthermodynamic and thermophysical properties, densities, viscosity, etc.,based on the formulation of the fluid. Production fluid is generally amixture of various hydrocarbons and other materials. An important aspectof oil and gas production operations is creating apressure-volume-temperature (PVT) model. A PVT model allows operatorsand other users to understand certain behavior or characteristics of theproduction fluid under certain conditions and at various stages. Forexample, a reservoir engineer may use the PVT model to estimate how muchoil/gas may be produced from the reservoir and how quickly the oil/gascan be produced. A process plant operator may use data from the PVT todetermine treatment processes for processing the fluid or for creatingintermediary products. An allocation engineer may use the PVT model tohelp determine allocation of the produced fluid.

In order to properly model the flow of oil from the reservoir into thewell and through production facilities (production piping, surfacepipelines, etc.), it is necessary to understand the thermodynamicproperties of the fluid, and to be able to calculate them to a certaindegree of accuracy. These properties can often vary quite a bit evenwithin a reservoir depending on the zone or well as well as over time.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present disclosure are described indetail below with reference to the attached drawing figures, which areincorporated by reference herein and wherein:

FIG. 1 illustrates a production well system, in accordance with exampleembodiments of the present disclosure;

FIG. 2 is a high level system diagram of a PVT modeling system, inaccordance with example embodiments of the present disclosure;

FIG. 3 illustrates a multiple well system instrumented with a PVTmodeling system, in accordance with example embodiments of the presentdisclosure; and

FIG. 4 is a high level system diagram of a multiple well PVT modelingsystem, in accordance with example embodiments of the presentdisclosure.

The illustrated figures are only exemplary and are not intended toassert or imply any limitation with regard to the environment,architecture, design, or process in which different embodiments may beimplemented.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The present disclosure is directed towards novel systems and methods forcreating a pressure-volume-temperature (PVT) model and characterizationof production fluid from fluid measurements obtained directly from thewell, including at the point of influx from the reservoir, throughoutthe wellbore, and through the associated surface facilities. Typically,in order to create a PVT model for a particular reservoir or wellsystem, a sample of production fluid is taken from the reservoir andbrought into a lab facility for analysis. However, it can be difficultto keep the sample in its downhole condition during transport andanalysis. Additionally, the sampled production fluid may not berepresentative of the entire reservoir as different areas of thereservoir may produce fluid having different properties. The propertiesof the production fluid may also change over time, and since there is asignificant delay between obtaining the fluid sample and receiving thePVT model from the lab analysis, the PVT model may be out of date. Toaddress these challenges, the present disclosure employs a system ofdownhole and surface sensors, such as multi-phase flow meters, pressuresensors, and temperature sensors, to obtain production fluidcharacteristics, which are used to generate a PVT model of the fluid onthe fly. Thus, a PVT model is produced with production fluidcharacteristics taken under the proper environmental conditions, andwhich has minimum time delay. Further, the up-to-date-PVT model can besynchronized across applications and sent to “customers” who use themodel for their needs, such as process plants, allocation engineers,reservoir engineers, etc. The present techniques can be used to generateblack oil PVT models as well as compositional PVT models.

Turning now to the figures, FIG. 1 illustrates an example productionwell system 100. The well system 100 includes a well 102 formed within aformation 104. The well 102 may be a vertical wellbore as illustrated orit may be a horizontal or directional well. The formation 104 may bemade up of several zones which may include oil reservoirs. In certainexample embodiments, the well system 100 may include a production tree108 and a wellhead 109 located at a well site 106. A production tubing112 extends from the wellhead 109 into the well 102. The productiontubing 112 includes a plurality of perforations 126 through which fluidsfrom the formation 104 can enter the production tubing 112 and flowupward into the production tree 108. In some embodiments, the subsurfacepressure on the fluids is large enough to push the fluid upwardnaturally. In some other embodiments, the production fluid is recoveredusing artificial lift or enhanced recovery techniques.

In some embodiments, the wellbore 102 is cased with one or more casingsegments 130. The casing segments 130 help maintain the structure of thewell 102 and prevents the well 102 from collapsing in on itself. In someembodiments, a portion of the well is not cased and may be referred toas “open hole”. The space between the production tubing 112 and thecasing 130 or wellbore 102 is an annulus 110. Production fluids enterthe annulus 110 from the formation 104 and then enter the productiontubing 112 from the annulus 110. Production fluid enters the productiontree 108 from the production tubing 112. The production fluid is thendelivered to various surface facilities for processing via a surfacepipeline 114.

It should be appreciated that well system 100 is only an example wellsystem and there are many other well system configurations may also beappropriate for use.

In some embodiments, the well system 100 includes one or more downholesensors 116. The sensors 116 measure one or more conditions of theproduction fluid in the downhole environment. This data is used ingenerating the PVT model. In some embodiments, the sensors 116 arecoupled to the outside of the production tubing 112 near the targetformation. In some other embodiments, the sensors 116 can be locatedinside the production tubing 112, on the borehole 102 wall, or otherwisedisposed downhole. In some embodiments, the sensors 116 may include aflow meter, a pressure sensor, a temperature sensor, a fluid compositionsensor, or any combination thereof, among other types of sensors.

The flow meter measures the rate of fluid flow into the productiontubing 112 downhole near the perforations 126. The pressure sensormeasures the amount of production fluid pressure downhole near theperforations 126. The temperature sensor measures the temperature of theproduction fluid downhole near the perforations 126. The fluidcomposition sensor detects the chemical makeup of the production fluiddownhole near the perforations 126. Depending on the operation and thedesired PVT model, the well system 100 may include any one of thesesensors, any combination of these sensors, or other types of sensors.

In some embodiments, the well system 100 includes one or more surfacesensors 118 configured to measure properties of production fluid at thesurface. This data is used in generating the PVT model. In someembodiments, the sensors 118 are coupled to a surface pipeline 114. Insome embodiments, the sensors 118 may include a multiphase flow meter, apressure sensor, a temperature sensor, a fluid composition sensor, orany combination thereof, among other types of sensors.

The flow meter measures the rate of fluid flow through the pipeline 114.The pressure sensor measures the amount of fluid pressure in thepipeline 114. The temperature sensor measures the temperature of theproduction fluid in the pipeline 114. The fluid composition sensordetects the chemical makeup of the production fluid in the pipeline 114.Depending on the operation and the desired PVT model, the well system100 may include any one of these sensors disposed above ground, anycombination of these sensors, or other types of sensors. In someembodiments, the well system 100 may only include one or more downholesensors 116 and no surface sensors. In some other embodiments, the wellsystem 100 may only include one or more surface sensors 118.

FIG. 2 is a high level system diagram of a PVT modeling system 200, inaccordance with example embodiments of the present disclosure. In someembodiments, a processor 202 receives at least one downhole sensor data204 collected from the downhole sensors 116 and/or at least one surfacesensor data 206 from the surface sensors 118 and generates a PVT model208 from the data. In some embodiments, the data 204, 206 is received inreal time or quasi-real time. In some embodiments, the processor 202receives the data 204, 206 through a direct wired connection. In someembodiments, the processor 202 receives the data 204, 206 through awireless communication protocol such as Wi-Fi, Bluetooth, cellularnetwork, and the like. In some embodiments, the processor 202 is coupledto or integral with one or more of the sensors 116, 118. The processor202 may be located downhole or above ground. In some embodiments theprocessor 202 is disposed at the well site 106 as a computing device oras a part of a control station. In some embodiments, the sensors 116,118 are coupled to a transmitter or a transceiver, which communicatesthe data from the sensors 116, 118 to the processor 202. In someembodiments, the processor 202 can be remotely located from the sensors116, 118 in a facility such as an office building or laboratory.

The processor 202 is communicatively coupled to or integrally includes amemory device. The memory device holds instructions for building a PVTmodel 208 using the data 204, 206 as inputs. The PVT model 208 can begenerated using various PVT modeling algorithms. The PVT modelingalgorithms may be considered a PVT model builder or a system to whichone or more inputs are applied. In some embodiments, the processor 202utilizes the sensor data 202, 206 as well as one or more previouslymeasured or known parameters with the PVT modeling algorithm.

In some embodiments, the PVT model 208 is a black-oil model, in whichthe composition of the production fluid is not taken into account. Inone such example embodiment, the sensor data 202, 206 includes adownhole flow rate, a surface flow rate, a downhole fluid temperature, asurface fluid temperature, a downhole fluid pressure, a surface fluidpressure, a gas rate, an oil rate, a water rate, a gas gravity, a watersalinity, or any combination of such. In some embodiments, the PVTmodeling algorithm for a black-oil PVT model includes the followingequations:

$\begin{matrix}{\mspace{79mu} {p_{r} = \frac{p}{p_{b}}}} & {{Eq}.\mspace{14mu} 1} \\{\mspace{79mu} {R_{sr} = \frac{R_{s}}{R_{sb}}}} & {{Eq}.\mspace{14mu} 2} \\{\mspace{79mu} {R_{sr} = {{a_{1}p_{r}^{a_{2}}} + {\left( {1 - a_{1}} \right)p_{r}^{a_{3}}}}}} & {{Eq}.\mspace{14mu} 3} \\{\mspace{79mu} {a_{1} = {f_{1}\left( {T,\gamma_{API},\gamma_{g},p_{b}} \right)}}} & {{Eq}.\mspace{14mu} 4} \\{\mspace{79mu} {a_{2} = {f_{2}\left( {T,\gamma_{API},\gamma_{g},p_{b}} \right)}}} & {{Eq}.\mspace{14mu} 5} \\{\mspace{79mu} {a_{3} = {f_{3}\left( {T,\gamma_{API},\gamma_{g},p_{b}} \right)}}} & {{Eq}.\mspace{14mu} 6} \\{B_{o} = {1.023761 + {0.000122\left\lbrack {{R_{s}^{0.413179}\gamma_{g}^{0.210293}\gamma_{API}^{0.127123}} + {0.019073\; T}} \right\rbrack}^{2.465976}}} & {{Eq}.\mspace{14mu} 7} \\{\mspace{79mu} {B_{g} = \frac{Z_{bh}T_{bh}p_{std}}{Z_{std}T_{std}p_{bh}}}} & {{Eq}.\mspace{14mu} 8} \\{\mspace{79mu} {p_{b} = {1091.47\left\lbrack {{R_{s}^{0.081465}\gamma_{g}^{- 0.161488}10^{X}} - 0.740152} \right\rbrack}^{5.354891}}} & {{Eq}.\mspace{14mu} 9} \\{\mspace{79mu} {X = {\left( {0.013098\; T^{0.282372}} \right) - \left( {8.2 \times 10^{- 6}\gamma_{API}^{2.176124}} \right)}}} & {{Eq}.\mspace{14mu} 10} \\{\mspace{79mu} {{\frac{\overset{.}{m_{o,{wh}}}}{\rho_{o,{wh}}}\frac{1}{B_{o,{wh}}}} = {N_{p}B_{o,{bh}}}}} & {{Eq}.\mspace{14mu} 11} \\{\mspace{79mu} {\frac{{\overset{.}{m}}_{o}}{\rho_{o}} = N_{p}}} & {{Eq}.\mspace{14mu} 12} \\{\mspace{79mu} {\frac{{\overset{.}{m}}_{g}}{\rho_{g}} = {N_{p}\left( {R_{p} - R_{s}} \right)}}} & {{Eq}.\mspace{14mu} 13} \\{{N_{p}\left\lbrack {B_{o} + {\left( {R_{p} - R_{s}} \right)B_{g}}} \right\rbrack} = {{{NB}_{oi}\left\lbrack {\frac{\left( {B_{o} - B_{oi}} \right) + {\left( {R_{si} - R_{s}} \right)B_{g}}}{B_{oi}} + {m\left( {\frac{B_{g}}{B_{gi}} - 1} \right)} + {\left( {1 + m} \right)\left( \frac{{c_{w}S_{wc}} + c_{f}}{1 - S_{wc}} \right)\Delta \; p}} \right\rbrack} + {\left( {W_{e} - W_{p}} \right)B_{w}}}} & {{Eq}.\mspace{14mu} 14} \\{\mspace{79mu} {B_{w} \approx 1}} & {{Eq}.\mspace{14mu} 15} \\{\mspace{79mu} {\mu_{o} = {10^{Y} - 1}}} & {{Eq}.\mspace{14mu} 16} \\{\mspace{79mu} {Y = {10^{2.9924 - {0.11027\; \gamma_{API}}}T^{- 0.9863}}}} & {{Eq}.\mspace{14mu} 17} \\{\mspace{79mu} {\mu_{g} = {10^{- 4}k_{v}{\exp \left( {x_{v}\left( \frac{\rho_{g}}{62.4} \right)}^{y_{v}} \right)}}}} & {{Eq}.\mspace{14mu} 18} \\{\mspace{79mu} {k_{v} = \frac{\left( {9.4 + {0.02\; {MW}_{g}}} \right)T^{1.5}}{209 + {19{MW}_{g}} + T}}} & {{Eq}.\mspace{14mu} 19} \\{\mspace{79mu} {y_{v} = {2.4 - {0.2\; x_{v}}}}} & {{Eq}.\mspace{14mu} 20} \\{\mspace{79mu} {x_{v} = {3.5 + \frac{986}{T} + {0.01{MW}_{g}}}}} & {{Eq}.\mspace{14mu} 21} \\{\mspace{79mu} {p_{r} = {p_{bh}/p_{b}}}} & {{Eq}.\mspace{14mu} 22}\end{matrix}$

In certain example embodiments, the processor 202 runs these equationsas a system of equations and solves for the following variables:

-   -   R_(s)=Solution Gas Oil Ratio    -   p_(b)=Bubble point pressure    -   B_(o)=Oil Formation Volume Factor    -   μ_(o)=Oil Viscosity    -   μ_(g)=Gas Viscosity

Such variables are the PVT model parameters and thus define the PVTmodel. All other variables are either known in advance or calculatedexplicitly as a part of the process.

In some embodiments, the PVT model is a compositional model, which takesinto account the composition of the production fluid. In one suchexample embodiment, the sensor data 202, 206 includes a downhole fluidpressure, a downhole fluid temperature, a surface fluid pressure, and asurface fluid temperature. The values of ρ_(o) (oil density) and ρ_(g)(gas density) will be measured by surface sensors. The overallcomposition (z_(i) for each component i) of the fluid at bottom holeconditions is measured by the fluid composition sensor. In someembodiments, the PVT modeling algorithm for a compositional PVT modelincludes the following equations:

In some embodiments, the approximate equilibrium ratio for eachcomponent is measured using a correlation, for example:

$\begin{matrix}{K_{i} = {\frac{p_{ci}}{p}{\exp \left\lbrack {5.37\left( {1 + \omega_{i}} \right)\left( {1 - \frac{T_{ci}}{T}} \right)} \right\rbrack}}} & {{Eq}.\mspace{14mu} 23}\end{matrix}$

In some embodiments, an initial value for the number of moles in the gasphase, n_(v), is found using the following equations.

$\begin{matrix}{n_{v} = \frac{\alpha}{\alpha - \beta}} & {{Eq}.\mspace{14mu} 24} \\{\alpha = {\sum\limits_{i}\; {z_{i}\left( {K_{i} - 1} \right)}}} & {{Eq}.\mspace{14mu} 25} \\{\beta = {\sum\limits_{i}\frac{z_{i}\left( {K_{i} - 1} \right)}{K_{i}}}} & {{Eq}.\mspace{14mu} 26}\end{matrix}$

In some embodiments, a nonlinear root finding algorithm, such asNewton-Raphson, is used to converge to a final value. In someembodiments, the following system of equations is solved for the phasecompositions (gas composition y_(i) for each component i and liquidcomposition x_(i) for each component i).

$\begin{matrix}{{\sum\limits_{i}\frac{z_{i}\left( {K_{i} - 1} \right)}{{n_{v}\left( {K_{i} - 1} \right)} + 1}} = 0} & {{Eq}.\mspace{14mu} 27} \\{n_{l} = {1 - n_{v}}} & {{Eq}.\mspace{14mu} 28} \\{x_{i} = \frac{z_{i}}{n_{l} + {n_{v}K_{i}}}} & {{Eq}.\mspace{14mu} 29} \\{y_{i} = {x_{i}K_{i}}} & {{Eq}.\mspace{14mu} 30}\end{matrix}$

In some embodiments, an equation of state is used to compute the liquidand vapor phase densities.

$\begin{matrix}{{f(Z)} = {{Z^{3} - Z^{2} + {\left( {A - B - B^{2}} \right)Z} - {AB}} = 0}} & {{Eq}.\mspace{14mu} 31} \\{A = \frac{a_{m,\phi}p}{R^{2}T^{2.5}}} & {{Eq}.\mspace{14mu} 32} \\{B = \frac{b_{m,\phi}p}{RT}} & {{Eq}.\mspace{14mu} 33} \\{{\phi = g},l} & {{Eq}.\mspace{14mu} 34} \\{a_{m,g} = \left\lbrack {\sum\limits_{i}{y_{i}\sqrt{a_{i}}}} \right\rbrack^{2}} & {{Eq}.\mspace{14mu} 35} \\{b_{m,g} = {\sum\limits_{i}{y_{i}b_{i}}}} & {{Eq}.\mspace{14mu} 36} \\{a_{m,l} = \left\lbrack {\sum\limits_{i}{x_{i}\sqrt{a_{i}}}} \right\rbrack^{2}} & {{Eq}.\mspace{14mu} 37} \\{b_{m,l} = {\sum\limits_{i}{x_{i}b_{i}}}} & {{Eq}.\mspace{14mu} 38} \\{a_{i} = {\Omega_{a}\frac{R^{2}T_{ci}^{2}}{p_{ci}}}} & {{Eq}.\mspace{14mu} 39} \\{b_{i} = {\Omega_{b}\frac{R^{2}T_{ci}}{p_{ci}}}} & {{Eq}.\mspace{14mu} 40}\end{matrix}$

The largest root of f(Z) will be the liquid phase compressibilityfactor, Z_(l), and the smallest root will be the gas phasecompressibility factor, Z_(g). The individual phase densities iscalculated using:

$\begin{matrix}{\rho_{l} = \frac{p{\sum{x_{i}{MW}_{i}}}}{Z_{l}{RT}}} & {{Eq}.\mspace{14mu} 41} \\{\rho_{g} = \frac{p{\sum{y_{i}{MW}_{i}}}}{Z_{g}{RT}}} & {{Eq}.\mspace{14mu} 42}\end{matrix}$

The calculated densities are compared with those measured using themultiphase flow meters. If the densities differ by more than a specifiedtolerance, a_(i) and b_(i) can be tuned by adjusting the factors Ω_(a)and Ω_(b). This can be done using a nonlinear root finding algorithm onfunctions of the form:

ρ_(φ,meas)−ρ_(φ,calc)=0  Eq. 43

Viscosities may be calculated using the following equations once thedensities are known.

$\begin{matrix}{\mu_{o} = {10^{Y} - 1}} & {{Eq}.\mspace{14mu} 44} \\{Y = {10^{2.9924 - {0.11027\; \gamma_{API}}}T^{- 0.9863}}} & {{Eq}.\mspace{14mu} 45} \\{\; {\mu_{g} = {10^{- 4}k_{v}{\exp \left( {x_{v}\left( \frac{\rho_{g}}{62.4} \right)}^{y_{v}} \right)}}}} & {{Eq}.\mspace{14mu} 46} \\{k_{v} = \frac{\left( {9.4 + {0.02\; {MW}_{g}}} \right)T^{1.5}}{209 + {19{MW}_{g}} + T}} & {{Eq}.\mspace{14mu} 47} \\{y_{v} = {2.4 - {0.2\; x_{v}}}} & {{Eq}.\mspace{14mu} 48} \\{\; {x_{v} = {3.5 + \frac{986}{T} + {0.01{MW}_{g}}}}} & {{Eq}.\mspace{14mu} 49}\end{matrix}$

All other variables may be considered to be either known in advance, orelse to be parameters that are calculated explicitly as part of theprocess. The solved variables are the PVT model parameters and definethe PVT model. The equations and algorithms used in the examples aboveare purely illustrative and are not limiting. In practice, the data 204,206 received from the downhole sensors 116 and surface sensors 118 canbe manipulated and applied in various different ways to generate a PVTmodel 208. However, by utilizing such data directly from the well system100, the parameters of the PVT model 208 calculated therefrom are moreaccurate. Thus, the generated PVT model 208 is more accurate as well.

In some embodiments, the PVT model 208 is generated by the processor 202and published or sent to various receiving parties. In some embodiments,the parameters of the PVT model 208 are generated by the processor 202and sent to another data processing means which generates the PVT model208 from the parameters. In some embodiments, the PVT model parametersor the PVT model 208 can be directly sent to one or more recipients. Insome embodiments, the PVT model 208 can be updated in real time orquasi-real time with up-to-date data 204, 206 measured by the sensors116, 118. In some embodiments, the PVT model 208 is updated when one ormore of the sensed measurements changes by a predetermined amount.

FIG. 3 illustrates a multiple well system 300, in accordance withexample embodiments of the present disclosure. The multiple well system300 includes a plurality of individual production well systems 302 suchas a first well system 302 a, a second well system 302 b, and a thirdwell system 302 c. Each of the well systems 302 is similar to the wellsystem 100 of FIG. 1, and includes a wellbore 304, a production tubing312, a production tree 308, a wellhead 309, and a surface pipeline 314coupled to a main pipeline 320. In some embodiments, production fluidrecovered from each of the wells 304 flows into the main pipeline 320which delivers the combined production fluid to a facility.

In some embodiments, one or more of the well systems 302 a, 302 b, 302 cis instrumented with one or more downhole sensors 316. The one or moredownhole sensors 316 may be coupled to a portion of the productiontubing, the wellbore, or elsewhere near the production formation. Insome embodiments, the downhole sensors 316 may include a multiphase flowmeter, a pressure sensor, a temperature sensor, a fluid compositionsensor, or any combination thereof, among other types of sensors.

In some embodiments, one or more of the well systems 302 is instrumentedwith one or more surface sensors 318. In some embodiments, the surfacesensors 318 can be coupled to the respective surface pipelines 314,production trees 308, or other surface portion of the well system 302through which production fluid flows. In some embodiments, the surfacesensors 318 may include a multiphase flow meter, a pressure sensor, atemperature sensor, a fluid composition sensor, or any combinationthereof, among other types of sensors. In some embodiments, one or moreof the well systems 302 may include a fluid composition sensor. Thedownhole and/or surface temperature, pressure, flow rates, composition,and other fluid characteristic of each well can be measured.

In some embodiments, one or more main line sensors 322 are coupled tothe main pipeline 320. The one or more main line sensors 322 may includea multiphase flow meter, a pressure sensor, a temperature sensor, afluid composition sensor, or any combination thereof, among other typesof sensors. The one or more main line sensors 322 can measure thecombined production fluid temperature, pressure, flow rate, composition,or any combination thereof.

It should be appreciated that multiple well system 300 is only anexample well system and there are many other well system configurationsmay also be appropriate for use.

FIG. 4 is a high level system diagram of a multiple well PVT modelingsystem 400, in accordance with example embodiments of the presentdisclosure. In some embodiments, one or more pieces of data collectedfrom each well system 402, such as flow rate 404 is transmitted to aprocessor 410. In some embodiments, each well system 402 generates anindividual PVT model 406, such as in the fashion illustrated anddescribed above with reference to FIGS. 1 and 2. In certain suchembodiments, the generated individual well PVT model 406 is transmittedto the processor 410. In certain example embodiments, one or moremeasured data from the main line sensor 322, such as flow rate 408, istransmitted to the processor 410. In some embodiments, data transmissionis via wired or wireless communication protocols, such as Bluetooth,cellular networks, Wi-Fi, and the like.

The processor 410 utilizes the data from each individual well 302 aswell as the main pipeline sensor 322 to generate a mixture PVT model412. The mixture PVT model 412 models the combined production fluid. Themixture PVT model 412 can be a black oil model or a compositional model.In some embodiments, the individual wells 302 do not generate individualPVT models. In certain such embodiments, the data collected from thedownhole and/or surface sensors are transmitted to the processor togenerate the mixture PVT model 412. The mixture PVT model 412 can thenbe transmitted to or accessed by one or more users. In some embodiments,the mixture PVT model 412 can be updated on the fly, based on apredetermined time interval, upon a certain condition, or on demand.

In addition to the embodiments described above, many examples ofspecific combinations are within the scope of the disclosure, some ofwhich are detailed below:

Example 1

A pressure-volume-temperature (PVT) modeling system for modelingproduction fluid from a well system, comprising:

-   -   a sensing device configured to obtain a measurement of a        condition of the production fluid in a downhole portion or in a        surface portion of the well system; and    -   a processor configured to receive the measurement from the        sensing device, apply the fluid measurement as an input into a        PVT model builder, and generate a PVT model.

Example 2

The PVT modeling system of example 1, wherein the measurement of acondition of the fluid includes at least one of downhole flow rate,surface flow rate, downhole fluid temperature, surface fluidtemperature, downhole fluid pressure, surface fluid pressure, gas rate,oil rate, water rate, gas gravity, water salinity, or any combination ofsuch.

Example 3

The PVT modeling system of example 1, wherein the PVT model builderincludes a system of equations configured to use the fluid measurementand known parameters of the well system to solve for a set of PVT modelparameters.

Example 4

The PVT modeling system of example 3, wherein the PVT model is definedby the PVT parameters.

Example 5

The PVT modeling system of example 3, wherein the PVT parameters includea solution gas ratio, a bubble point pressure, an oil formation volumefactor, an oil viscosity, a gas viscosity, or any combination of such.

Example 6

The PVT modeling system of example 1, wherein the PVT model is ablack-oil PVT model.

Example 7

The PVT modeling system of example 1, wherein the PVT model is acompositional PVT model.

Example 8

The PVT modeling system of example 1, wherein the PVT model is updatedwhen the fluid measurement changes.

Example 9

A method of generating a PVT model for a production fluid from a wellsystem, comprising:

-   -   receiving, from a sensing device, a measurement of a condition        of the production fluid in a downhole portion or a surface        portion of the well system; and    -   generating a PVT model from the measurement.

Example 10

The method of example 9, further comprising:

-   -   inputting the measurement into a PVT model builder.

Example 11

The method of example 10, further comprising:

-   -   determining a set of PVT parameters from the measurement; and    -   generating the PVT model of the production fluid using the PVT        parameters.

Example 12

The method of example 9, wherein the PVT model is a black-oil PVT model.

Example 13

The method of example 9, wherein the PVT model is a compositional PVTmodel.

Example 14

The method of example 9, wherein the measurement includes a downholeflow rate, a downhole fluid temperature, a downhole fluid pressure, orany combination of such received from a first sensing device disposedwithin the well.

Example 15

The method of example 9, wherein the measurement includes at least oneof surface flow rate, surface fluid temperature, surface fluid pressure,or any combination of such received from a second sensing device coupledto a surface pipeline.

Example 16

The method of example 10, further comprising:

-   -   inputting the measurement into a system of equations; and    -   solving the system of equations for the PVT parameters.

Example 17

The method of example 9, wherein the PVT model is generated inquasi-real time upon receiving the measurement.

Example 18

The method of example 9, comprising:

-   -   updating the PVT model when the measurement changes, at        predetermined times, or upon receiving a command.

Example 19

The method of example 11, further comprising determining the set of PVTparameters from the measurements and at least one known parameter of thewell system.

Example 20

The method of example 9, wherein the measurement of a condition of theproduction fluid includes at least one of downhole flow rate, surfaceflow rate, downhole fluid temperature, surface fluid temperature,downhole fluid pressure, surface fluid pressure, gas rate, oil rate,water rate, gas gravity, water salinity, or any combination of such.

This discussion is directed to various embodiments of the invention. Thedrawing figures are not necessarily to scale. Certain features of theembodiments may be shown exaggerated in scale or in somewhat schematicform and some details of conventional elements may not be shown in theinterest of clarity and conciseness. Although one or more of theseembodiments may be preferred, the embodiments disclosed should not beinterpreted, or otherwise used, as limiting the scope of the disclosure,including the claims. It is to be fully recognized that the differentteachings of the embodiments discussed may be employed separately or inany suitable combination to produce desired results. In addition, oneskilled in the art will understand that the description has broadapplication, and the discussion of any embodiment is meant only to beexemplary of that embodiment, and not intended to intimate that thescope of the disclosure, including the claims, is limited to thatembodiment.

Certain terms are used throughout the description and claims to refer toparticular features or components. As one skilled in the art willappreciate, different persons may refer to the same feature or componentby different names. This document does not intend to distinguish betweencomponents or features that differ in name but not function, unlessspecifically stated. In the discussion and in the claims, the terms“including” and “comprising” are used in an open-ended fashion, and thusshould be interpreted to mean “including, but not limited to . . . .”Also, the term “couple” or “couples” is intended to mean either anindirect or direct connection. The use of “top,” “bottom,” “above,”“below,” and variations of these terms is made for convenience, but doesnot require any particular orientation of the components.

Reference throughout this specification to “one embodiment,” “anembodiment,” or similar language means that a particular feature,structure, or characteristic described in connection with the embodimentmay be included in at least one embodiment of the present disclosure.Thus, appearances of the phrases “in one embodiment,” “in anembodiment,” and similar language throughout this specification may, butdo not necessarily, all refer to the same embodiment.

Although the present invention has been described with respect tospecific details, it is not intended that such details should beregarded as limitations on the scope of the invention, except to theextent that they are included in the accompanying claims.

We claim:
 1. A pressure-volume-temperature (PVT) modeling system formodeling production fluid from a well system, comprising: a sensingdevice configured to obtain a measurement of a condition of theproduction fluid in a downhole portion or in a surface portion of thewell system; and a processor configured to receive the measurement fromthe sensing device, apply the fluid measurement as an input into a PVTmodel builder, and generate a PVT model.
 2. The PVT modeling system ofclaim 1, wherein the measurement of a condition of the fluid includes atleast one of downhole flow rate, surface flow rate, downhole fluidtemperature, surface fluid temperature, downhole fluid pressure, surfacefluid pressure, gas rate, oil rate, water rate, gas gravity, watersalinity, or any combination of such.
 3. The PVT modeling system ofclaim 1, wherein the PVT model builder includes a system of equationsconfigured to use the fluid measurement and known parameters of the wellsystem to solve for a set of PVT model parameters.
 4. The PVT modelingsystem of claim 3, wherein the PVT model is defined by the PVTparameters.
 5. The PVT modeling system of claim 3, wherein the PVTparameters include a solution gas ratio, a bubble point pressure, an oilformation volume factor, an oil viscosity, a gas viscosity, or anycombination of such.
 6. The PVT modeling system of claim 1, wherein thePVT model is a black-oil PVT model.
 7. The PVT modeling system of claim1, wherein the PVT model is a compositional PVT model.
 8. The PVTmodeling system of claim 1, wherein the PVT model is updated when thefluid measurement changes.
 9. A method of generating a PVT model for aproduction fluid from a well system, comprising: receiving, from asensing device, a measurement of a condition of the production fluid ina downhole portion or a surface portion of the well system; andgenerating a PVT model from the measurement.
 10. The method of claim 9,further comprising: inputting the measurement into a PVT model builder;11. The method of claim 10, further comprising: determining a set of PVTparameters from the measurement; and generating the PVT model of theproduction fluid using the PVT parameters.
 12. The method of claim 9,wherein the PVT model is a black-oil PVT model.
 13. The method of claim9, wherein the PVT model is a compositional PVT model.
 14. The method ofclaim 9, wherein the measurement includes a downhole flow rate, adownhole fluid temperature, a downhole fluid pressure, or anycombination of such received from a first sensing device disposed withinthe well.
 15. The method of claim 9, wherein the measurement includes atleast one of surface flow rate, surface fluid temperature, surface fluidpressure, or any combination of such received from a second sensingdevice coupled to a surface pipeline.
 16. The method of claim 10,further comprising: inputting the measurement into a system ofequations; and solving the system of equations for the PVT parameters.17. The method of claim 9, wherein the PVT model is generated inquasi-real time upon receiving the measurement.
 18. The method of claim9, comprising: updating the PVT model when the measurement changes, atpredetermined times, or upon receiving a command.
 19. The method ofclaim 11, further comprising determining the set of PVT parameters fromthe measurements and at least one known parameter of the well system.20. The method of claim 9, wherein the measurement of a condition of theproduction fluid includes at least one of downhole flow rate, surfaceflow rate, downhole fluid temperature, surface fluid temperature,downhole fluid pressure, surface fluid pressure, gas rate, oil rate,water rate, gas gravity, water salinity, or any combination of such.